Cheng Haoxuan
Papers
1
Total Citations
6
H-Index
1
About
Cheng Haoxuan has carved a distinctive niche at the intersection of energy-efficient computing and autonomous robotics, with a primary focus on deploying high-performance artificial intelligence on resource-constrained hardware. His most cited work, "A Performance Per Power Efficient Object Detector on an FPGA for Robot Operating System (ROS)" (2018, 6 citations), exemplifies his core contribution: demonstrating that field-programmable gate arrays can deliver the real-time object detection required for service robots while dramatically reducing power consumption compared to traditional GPU-based solutions. This research directly addresses the growing demand for intelligent robots in homes and hospitals, where computational intensity must be balanced against battery life and thermal constraints. By integrating FPGA-accelerated vision into the widely-used ROS framework, Haoxuan has provided a practical pathway for deploying sophisticated perception in mobile and human-interactive robots. His work stands as a key reference for researchers seeking to bridge the gap between deep learning's computational demands and the strict power budgets of autonomous systems, establishing him as a thoughtful contributor to sustainable, edge-based robotics.
Research Focus
Key Achievements
Top Papers
- 1